mlsanityData to 14 Oct 2026

LLatrieval: LLM-Verified Retrieval for Verifiable Generation

arXiv 2311.07838 ↗First posted 14 Nov 2023cs.CL cs.AI cs.IRcited by 52

Not on the map. Why?

Cited by 23 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Verifiable generation aims to let the large language model (LLM) generate text with supporting documents, which enables the user to flexibly verify the answer and makes the LLM's output more reliable. Retrieval plays a crucial role in verifiable generation. Specifically, the retrieved documents not only supplement knowledge to help the LLM generate correct answers, but also serve as supporting evidence for the user to verify the LLM's output. However, the widely used retrievers become the bottleneck of the entire pipeline and limit the overall performance. Their capabilities are usually inferior to LLMs since they often have much fewer parameters than the large language model and have not been demonstrated to scale well to the size of LLMs. If the retriever does not correctly find the supporting documents, the LLM can not generate the correct and verifiable answer, which overshadows the LLM's remarkable abilities. To address these limitations, we propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can sufficiently support answering the question. Thus, the LLM can iteratively provide feedback to retrieval and facilitate the retrieval result to fully support verifiable generation. Experiments show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results.

Key facts. Cited by 52 papers in the map (data through 14 Oct 2026). Builds on 46 of its 65 references that are in the map. First posted on arXiv 14 Nov 2023.

Builds on

46 of 65 references.

References · 46

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
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  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
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  10. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  11. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  12. 2019Passage Re-ranking with BERTcited by 645
34 more
  1. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  2. 2019ELI5: Long Form Question Answeringcited by 575
  3. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  4. 2021Large Dual Encoders Are Generalizable Retrieverscited by 456
  5. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 376
  6. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  7. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  8. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 360
  9. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  10. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 333
  11. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
  12. 2023Evaluating Verifiability in Generative Search Enginescited by 303
  13. 2022Teaching language models to support answers with verified quotescited by 292
  14. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  15. 2021Measuring Attribution in Natural Language Generation Modelscited by 256
  16. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 251
  17. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 235
  18. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 234
  19. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 225
  20. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  21. 2023Can Large Language Models Really Improve by Self-critiquing Their Own Plans?cited by 108
  22. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  23. 2023WebCPM: Interactive Web Search for Chinese Long-form Question Answeringcited by 93
  24. 2023GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problemscited by 89
  25. 2021Adversarial Retriever-Ranker for dense text retrievalcited by 83
  26. 2023InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrievalcited by 73
  27. 2023A Survey of Large Language Models Attributioncited by 71
  28. 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpuscited by 60
  29. 2022Questions Are All You Need to Train a Dense Passage Retrievercited by 47
  30. 2022QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphscited by 42
  31. 2022Multi-View Document Representation Learning for Open-Domain Dense Retrievalcited by 41
  32. 2023Large Language Models are Strong Zero-Shot Retrievercited by 26
  33. 2023Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attributioncited by 24
  34. 2023Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representationscited by 20

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 21; 2025: 21; 2026: 10.

52 papers.

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  1. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 205
  2. 2024Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approachcited by 119
  3. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  4. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  5. 2024MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generationcited by 44
  6. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  7. 2024Improving Attributed Text Generation of Large Language Models via Preference Learningcited by 23
  8. 2024AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attributioncited by 23
  9. 2024Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generationcited by 21
  10. 2024Atomic Fact Decomposition Helps Attributed Question Answeringcited by 21
  11. 2025Reliable Text-to-SQL with Adaptive Abstentioncited by 20
  12. 2024Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metricscited by 17
38 more
  1. 2024Verifiable Generation with Subsentence-Level Fine-Grained Citationscited by 15
  2. 2025TokenShapley: Token Level Context Attribution with Shapley Valuecited by 15
  3. 2025VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robotscited by 14
  4. 2024CaLM: Contrasting Large and Small Language Models to Verify Grounded Generationcited by 11
  5. 2024Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answeringcited by 10
  6. 2025RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimizationcited by 9
  7. 2024RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Modelscited by 8
  8. 2024WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citationscited by 7
  9. 2025Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answeringcited by 6
  10. 2025Principled Context Engineering for RAG: Statistical Guarantees via Conformal Predictioncited by 6
  11. 2025On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systemscited by 5
  12. 2025Large Language Model Sourcing: A Surveycited by 5
  13. 2025Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agentscited by 4
  14. 2025Plan Verification for LLM-Based Embodied Task Completion Agentscited by 4
  15. 2024An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMscited by 3
  16. 2025ChartLens: Fine-grained Visual Attribution in Chartscited by 3
  17. 2025A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languagescited by 3
  18. 2025Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledgecited by 3
  19. 2025Look It Up: Analysing Internal Web Search Capabilities of Modern LLMscited by 3
  20. 2024A Comparative Analysis of Faithfulness Metrics and Humans in Citation Evaluationcited by 2
  21. 2024FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrievalcited by 2
  22. 2025Agentic Verification for Ambiguous Query Disambiguationcited by 2
  23. 2025Decomposing and Revising What Language Models Generatecited by 2
  24. 2026DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoningcited by 2
  25. 2025Inter-Passage Verification for Multi-evidence Multi-answer QAcited by 1
  26. 2025RoSE: Round-robin Synthetic Data Evaluation for Selecting LLM Generators without Human Test Setscited by 1
  27. 2026Rethinking RAG in Long Videos: What to Retrieve and How to Use It?cited by 1
  28. 2024On the Influence of Shape, Texture and Color for Learning Semantic Segmentationcited by 0
  29. 2024Contrast All the Time: Learning Time Series Representation from Temporal Consistencycited by 0
  30. 2024Assessing the Answerability of Queries in Retrieval-Augmented Code Generationcited by 0
  31. 2025A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generationcited by 0
  32. 2025Issue Retrieval and Verification Enhanced Supplementary Code Comment Generationcited by 0
  33. 2026Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Surveycited by 0
  34. 2026Dynamic Theory of Mind as a Temporal Memory Problem: Evidence from Large Language Modelscited by 0
  35. 2026Beyond Relevance: Utility-Centric Retrieval in the LLM Eracited by 0
  36. 2026Route Before Retrieve: Activating Latent Routing Abilities of LLMs for RAG vs. Long-Context Selectioncited by 0
  37. 2026RIMS: Preference Optimization via Smoothed Multi-pair Aggregation for Small-Scale LLM Retrieval-Augmented Generationcited by 0
  38. 2026D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Understandingcited by 0

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